{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "iris = load_iris()\n",
    "#print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HxD0x3utVu+d8n6qBfOezW/nmymspDm1JOSfqDfNG3+cAeKfwJaKeOvUAPHj4oJfbdfe9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\nzaVhZmaluTTMzKw0l4aZmZX2X5/vEKhk+8ZTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x853c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot()\n",
    "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y,  cmap = plt.cm.brg)\n",
    "plt.title('Logistic Regression')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
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